Papers

10

Total Citations

230

H-Index

7

About

Kevin Stone’s research bridges two seemingly distinct worlds: robotic manipulation and surgical skill assessment. In robotics, he tackles the notoriously difficult problem of deformable object manipulation, developing learning-based policies that enable robots to handle ropes, cables, and hoses—work that has garnered over 100 citations. His mobile manipulation systems, capable of one-shot task learning from a single VR demonstration, bring autonomous robots into real homes for complex, human-level chores. Stone also advances perception with stereo depth systems that handle challenging surfaces like glass and metal, enabling robust manipulation in unstructured environments. In the medical domain, he applies his robotics expertise to surgery, co-creating the Robotic Anastomosis Competency Evaluation (RACE) tool to objectively assess surgical skills during robot-assisted procedures. His studies on parastomal hernia outcomes and surgeon non-technical skills have shaped training and teamwork in the operating room. With over 230 citations across his portfolio, Stone’s work exemplifies how robotic learning and surgical innovation can inform each other—teaching robots to handle a rope or helping surgeons master a stitch with equal rigor.

Research Focus

Key Achievements

7
H-Index
10
Papers
230
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
103 citations · 2020
📈 Most Prolific Year: 2020 (3 Papers)
🤝 Key Collaborators: 53
🏛 Institutions: Toyota Research Institute, Roswell Park Comprehensive Cancer Center, University of Missouri

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago